Pixel labeling is typically used to indicate “what is being described” by a respective pixel. For example, pixel labeling may be used to indicate whether a pixel is included in a foreground or background of an image. Pixel labeling may also be used to describe an object that includes the pixel (e.g., text, a person, or other object or graphic elements). The labeling of the pixels may be used to support a variety of functionality, such as object removal to remove those pixels that correspond with a particular object, optical character recognition in which pixels are processed that are labeled as corresponding to text, auto layer separation in which different layers are formed from an image for each object included in the image to ease editing of the image, and so forth.
One technique that is used to perform pixel labeling involves use of a model generated through machine learning. Machine learning is used to train the model by using training data that includes images and labeled objects, e.g., text, foreground, and so forth. In this way, the model is trained by learning patterns as to which pixels represent the labeled objects in the training data.
Conventional techniques used to form the training data, however, often include inaccuracies that are then propagated to the model, and thus result in inaccuracies of the model itself. For example, one such conventional technique requires users to manually label the objects in the images by manually defining a border of the objects within the images. Thus, this training data is dependent on the user's manual dexterity in defining the border (e.g., in drawing the border using a tool similar to a pencil in a user interface). Accordingly, this manually drawn border may include pixels that are not actually considered part of an object being defined, and thus may cause inaccurate training of the model based on inaccurate labeling of those pixels. Further, defining a border in such a manner may take a significant amount of time, and thus sets of training data may be limited by a number of examples that are made available. Such limited training data may also limit the accuracy of the model that is trained using this data. As a result, a significant expense to obtain a sufficient number of examples may be required.